방법 비교

	BERT 임베딩	Vision Transformer
분야	텍스트 마이닝	딥러닝
계열	Process / pipeline	Machine learning
기원 연도	2019	2021
창시자	Devlin, Chang, Lee & Toutanova (Google AI)	Dosovitskiy, A. et al.
유형	Contextual transformer text-representation method	Transformer architecture for images (self-attention over patches)
원전	Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. NAACL-HLT, 4171-4186.	Dosovitskiy, A. et al. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. ICLR.
별칭	contextual embeddings, transformer embeddings, BERT Tabanlı Metin Gömülmeleri	Görsel Transformer (ViT), görsel transformer, ViT, patch transformer for images
관련	4	5
요약	BERT-based text embeddings, introduced by Devlin and colleagues at Google AI in 2019, turn text into context-sensitive dense vectors using a bidirectional Transformer encoder. Because the meaning of a word shifts with its context, BERT produces richer representations than static methods such as Word2Vec or topic models like LDA.	The Vision Transformer (ViT), introduced by Dosovitskiy and colleagues in 2021, splits an image into fixed-size patches, treats those patches as a sequence, and applies the Transformer self-attention mechanism to image classification. Given enough training data, it surpasses convolutional neural networks (CNNs).